Keyword Searches vs. AI Matching: Which Drives Results?
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To understand why AI has captured so much attention, one must first understand what it actually brings to the table.
n recent years, talent acquisition professionals have been inundated with new technologies, all of which promise to deliver qualified candidates with unprecedented efficiency and ease. Most talkedabout among these game changers is artificial intelligence (AI). To understand just why AI has captured so much attention, one must first understand what it actually Martin brings to the table. Let's take a look at how Genesys's purpose-built AI compares to those old recruiter standbys of resume keyword searches and Boolean search strings when it comes to finding the best-fit candidates for a job. Words Have Meanings, Which AI Uncovers Traditional keyword searches simply identify the presence of certain words in a candidate's resume or profile. On the other hand, Genesys's AI-powered search and match functions can identify the context of a combination of words and phrases based on the proximity of certain words to other words. Context matters in searches. The manner in which a given set of words are strung together makes a huge difference in the overall meaning of those words. For example, say you are looking to fill a position for a "Microsoft certified network security specialist." A basic keyword search will certainly return profiles of candidates who fit Recruiter.com Magazine
the bill. However, that search might also return a "security" guard "certified" in CPR, proficient in "Microsoft" Word, and with previous experience working in the lobby of a national cable news "network."
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The security guard would never make it through the screening process. A human recruiter would quickly identify that candidate as a bad match, but that identification process is a waste of precious time.
This is where AI comes in. Latent semantic analysis — a machine-learning model for extracting and representing the contextualusage meaning of words through statistical computations applied to a large body of resumes — would also quickly determine the candidate was not a match. The difference in this case is the technology filters the bad match out before it ever gets in front of a human. Through latent semantic analysis, Genesys's technology greatly reduces the number of false positives returned by traditional Boolean and keyword searches. In doing so, it also minimizes the resources needed to qualify candidates and saves time by eliminating the need for multiple search queries. AI Offers an Easier Input Finding the right talent to fill a job opening is a very re s u l t s - o r i e n t e d
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